{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ]\n",
    "    )\n",
    "\n",
    "#训练数据集\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, \n",
    "                                       download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=16,\n",
    "                                         shuffle=True, num_workers=2)\n",
    "\n",
    "#测试数据集\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                      download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=16,\n",
    "                                        shuffle=False, num_workers=2)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "def imshow(img):\n",
    "    # 输入数据: torch.tensor [c, h, w]\n",
    "    img = img / 2+0.5\n",
    "    nping = img.numpy()\n",
    "    nping = np.transpose(nping, (1,2,0)) # [h,w,c]\n",
    "    plt.imshow(nping)\n",
    "    \n",
    "dataiter = iter(trainloader) #随机加载一个mini batch\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "imshow(torchvision.utils.make_grid(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self): #定义神经网络结构, 输入数据 3x32x32\n",
    "        super(Net, self).__init__()\n",
    "        # 第一层（卷积层）\n",
    "        self.conv1 = nn.Conv2d(3,6,3) #输入频道3， 输出频道6， 卷积3x3\n",
    "        # 第二层（卷积层）\n",
    "        self.conv2 = nn.Conv2d(6,16,3) #输入频道6， 输出频道16， 卷积3x3\n",
    "        # 第三层（全连接层）\n",
    "        self.fc1 = nn.Linear(16*28*28, 512) #输入维度16x28x28=12544，输出维度 512\n",
    "        # 第四层（全连接层）\n",
    "        self.fc2 = nn.Linear(512, 64) #输入维度512， 输出维度64\n",
    "        # 第五层（全连接层）\n",
    "        self.fc3 = nn.Linear(64, 10) #输入维度64， 输出维度10\n",
    "    \n",
    "    def forward(self, x): #定义数据流向\n",
    "        x = self.conv1(x)\n",
    "        x = F.relu(x)\n",
    "        \n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        \n",
    "        x = x.view(-1, 16*28*28)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        \n",
    "        x = self.fc3(x)\n",
    "        \n",
    "        return x\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (conv2): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=12544, out_features=512, bias=True)\n",
      "  (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
      "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.0001, momentum=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [50:48<00:00, 148.08s/it]\n"
     ]
    }
   ],
   "source": [
    "train_loss_hist = []\n",
    "test_loss_hist = []\n",
    "\n",
    "for epoch in tqdm(range(20)):\n",
    "    #训练\n",
    "    net.train()\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader):\n",
    "        images, labels = data        \n",
    "        outputs = net(images)\n",
    "        loss = criterion(outputs, labels) # 计算损失\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        if(i%250 == 0): #每250 mini batch 测试一次\n",
    "            correct = 0.0\n",
    "            total = 0.0\n",
    "\n",
    "            net.eval()\n",
    "            with torch.no_grad():\n",
    "                for test_data in testloader:\n",
    "                    test_images, test_labels = test_data\n",
    "                    test_outputs = net(test_images)\n",
    "                    test_loss = criterion(test_outputs, test_labels)\n",
    "\n",
    "            train_loss_hist.append(running_loss/250)\n",
    "            test_loss_hist.append(test_loss.item())\n",
    "            running_loss=0.0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(temp)\n",
    "plt.plot(test_loss_hist)\n",
    "plt.legend(('train loss', 'test loss'))\n",
    "plt.title('Train/Test Loss')\n",
    "plt.xlabel('# mini batch *250')\n",
    "plt.ylabel('Loss')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
